\section{Approach}

The state space for our domain is very big. To shrink our state space for
feasability of the approach, our approach uses the Google PageRank algorithm.
This gives us a a list of web pages for a given subject sorted by popularity.
To use Google's PageRank algorithm, Google provides us with a web service API
\cite{GoogleSearchAPI}. 

After obtaining the sorted list we start extracting specific attributes of each
web page. The approach is indifferent to what exact attributes of each web page
that is extracted. These attributes of each webpage are used to train the naive
bayes classifier on the frequency of occurrence of each of them. 

Once all the desired attributes of the web page have been extracted, we train a
naive bayes classifier using the extracted attributes for each web page as a
training set. To training the naive bayes classifier, it keeps an updated count
of frequency of each attribute over the set of all webpages returned form the
page rank search algorithm.

